Role of modeled high-grade glioma cell invasion and survival on the prediction of tumor progression after radiotherapy

<i>Objective.</i>Glioblastoma (GBM) prognosis remains poor despite progress in radiotherapy and imaging techniques. Tumor recurrence has been attributed to the widespread tumor invasion of normal tissue. Since the complete extension of invasion is undetectable on imaging, it is not deliberately treated. To improve the treatment outcome, models have been developed to predict tumor invasion based standard imaging data. This study aimed to investigate whether a tumor invasion model, together with the predicted number of surviving cells after radiotherapy, could predict tumor progression post-treatment.<i>Approach.</i>A tumor invasion model was applied to 56 cases of GBMs treated with radiotherapy. The invasion was quantified as the volume encompassed by the 100 cells mm^-3isocontour (<i>V</i>₁₀₀). A new metric, cell-volume-product, was defined as the product of the volume with cell density greater than a threshold value (in cells mm^-3), and the number of surviving cells within that volume, post-treatment. Tumor progression was assessed at 20 ± 10 d and 90 ± 20 d after treatment. Correlations between the disease progression and the gross tumor volume (GTV),<i>V</i>₁₀₀, and cell-volume-product, were determined using receiver operating characteristic curves.<i>Main results.</i>For the early follow-up time, the correlation between GTV and tumor progression was not statistically significant (<i>p</i>= 0.684). However, statistically significant correlations with progression were found between<i>V</i>₁₀₀and cell-volume-product with a cell threshold of 10^-6cells mm^-3with areas-under-the-curve of 0.69 (<i>p</i>= 0.023) and 0.66 (<i>p</i>= 0.045), respectively. No significant correlations were found for the late follow-up time.<i>Significance.</i>Modeling tumor spread otherwise undetectable on conventional imaging, as well as radiobiological model predictions of cell survival after treatment, may provide useful information regarding the likelihood of tumor progression at an early follow-up time point, which could potentially lead to improved treatment decisions for patients with GBMs.